050-0027/01 – Security in Information Technology (IvB)
| Gurantor department | Department of Civil Protection | Credits | 5 |
| Subject guarantor | doc. Ing. Pavel Šenovský, Ph.D. | Subject version guarantor | doc. Ing. Pavel Šenovský, Ph.D. |
| Study level | undergraduate or graduate | Requirement | Compulsory |
| Year | 1 | Semester | winter |
| | Study language | Czech |
| Year of introduction | 2019/2020 | Year of cancellation | |
| Intended for the faculties | FBI | Intended for study types | Follow-up Master |
Subject aims expressed by acquired skills and competences
Basic knowledge in the area of SCADA systems. The synthesis of knowledge from the software support of analyses from various data sources.
Teaching methods
Lectures
Tutorials
Project work
Summary
Students will familiarize themselves with the possibilities of employing the information technologies for database datamining as a useful source of information for an effective decision-making process. They will also familiarize with the rudiments of industrial automation of manufacturing process.
Compulsory literature:
R: The R Project for Statistical Computing [online]. [cit. 2018-09-4]. Dostupné z: https://www.r-project.org/
Recommended literature:
Additional study materials
Way of continuous check of knowledge in the course of semester
Zpracování semestrálního projektu, písemná zkouška
E-learning
textook and other study materials for the course are available in LMS system.
Other requirements
There are not defined aditional requirements to student.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1. Introduction to industrial automation
2-3. SCADA systems and its usage
4. Realtime databases and data processing in industrial automation
5. Actual security threats in industrial automation
6. Protection of industrial control systems
7. Datamining and its application in security
8. Decision trees
9. Associative rules
10. Bayes classificator
11. Introduction to neural networks
12. Introduction to deep learning
13. Evolutionary algorithms in datamining
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction